RX 9060 XT 16GBBudget pick
16 GB VRAM (+8)320 GB/s (+96)
C
Raises estimated decode speed by about 52%.41.3 tok/s decode
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Can aya expanse 8b orthogonal heretic run on Radeon Pro W7500 8GB?
YES — Tight Fit
C50Usable
○Estimated from fit model
aya expanse 8b orthogonal heretic needs ~7.5 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~27 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 7.5 GB, 27.1 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
27.1 tok/s
TTFT
7149 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsaya expanse 8b orthogonal heretic on Radeon Pro W7500 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 27.1 tok/s decode · 7.1s TTFT (warm) · 68 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 27.1 tok/s | 3899 ms | 24K |
| Coding | C | Tight fit | 27.1 tok/s | 7149 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 18.1 tok/s | 15576 ms | 24K |
| Reasoning | C | Tight fit | 27.1 tok/s | 8448 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 18.1 tok/s | 19470 ms |
Quantization options
How aya expanse 8b orthogonal heretic (8B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C53 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 |
Get started
Copy-paste commands to run aya expanse 8b orthogonal heretic on your machine.
Run
lms load hf-mradermacher--aya-expanse-8b-orthogonal-heretic-gguf && lms server startUpgrade options
Hardware that runs aya expanse 8b orthogonal heretic well
RX 7700 XT 12GBBest value
12 GB VRAM (+4)432 GB/s (+208)
C
Raises estimated decode speed by about 96%.53.1 tok/s decode
Raises estimated decode speed by about 96%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RX 6700 XT 12GBAMD upgrade
12 GB VRAM (+4)384 GB/s (+160)
C
Raises estimated decode speed by about 51%.40.9 tok/s decode
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP